AWSCloudGoogle Cloud PlatformGraphQLJavaScriptNext.jsNode.jsPostgresPythonReactRuby on RailsTypeScriptGoRubyAILLMOpenAIClaudeAnthropicRAGAgenticRailsGCPGoogle CloudPostgreSQLCI/CD
About this role
Role Overview
Implement Fuse end-to-end full stack features across our platform: APIs and services on the backend, and customer-facing dashboards and operator tools on the frontend.
Model and configure each client's lending domain in Fuse: loan types, underwriting rules, debt adjustment rules, task flows, adverse actions, and email/e-sign templates.
Build and maintain client-specific automations and workflows that orchestrate the end-to-end loan lifecycle, integrating with credit bureaus, KYC providers, banking rails, and document services.
Ship AI-powered features into production: LLM-based automations, classification and extraction pipelines, agentic workflows, and tool-using agents (e.g., MCP-style tool integrations) that operate on real customer data.
Own features end-to-end: scoping with implementation, testing, deployment, monitoring, and iteration based on production signals.
Write clean, well-tested, observable code and contribute to our shared standards on code quality, reliability, and security.
Requirements
3+ years of professional experience as a software engineer, ideally in a full-stack role.
Strong proficiency with a modern backend stack (Node.js/TypeScript, Python, Go, or similar) and experience designing and consuming REST/GraphQL APIs.
Solid frontend experience with React (Next.js a plus), TypeScript, and modern state management; able to ship polished, responsive UIs.
Comfortable working with relational databases (PostgreSQL), writing non-trivial queries, and thinking about data modeling for transactional systems.
Experience deploying services to the cloud (AWS, GCP, or similar), with a working understanding of CI/CD, observability, and basic operational practices.
Comfortable working in English, both written and spoken; able to collaborate asynchronously with a distributed team.
Hands-on experience integrating LLMs (OpenAI, Anthropic, or equivalent) into production systems not just prototypes or notebooks.
Comfort with prompt engineering, structured outputs, tool/function calling, evaluation, and guardrails for AI-driven features.
Experience with at least one of: retrieval-augmented generation (RAG), agentic workflows, MCP servers/clients, vector search, or LLM observability.
Pragmatic understanding of when to use AI versus deterministic logic, including cost, latency, reliability, and risk trade-offs in regulated environments such as lending.
Daily use of AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) with a clear point of view on where they help and where they hurt.
Tech Stack
AWS
Cloud
Google Cloud Platform
GraphQL
JavaScript
Next.js
Node.js
Postgres
Python
React
Ruby on Rails
TypeScript
Go
Benefits
Competitive compensation
Full AI tooling stack covered
Remote-first culture with strong autonomy
Real impact on the financial infrastructure used by credit unions and lenders across the U.S.
Direct impact on the product roadmap and on how lending gets done in the region.